The direct chemical analysis allows investigating samples without altering them, keeping the sample available for further analysis. For the qualitative investigation, analytical procedures like gas-chromatography, Raman microscopy, and Infra Red spectroscopy are available; however, the univariate approach is not exhaustive in the case of very complex matrices. The quantitative approach is still an open issue, due to the strong matrix effect hindering the creation of univariate calibration methods in interpolation mode. The multivariate analysis may be the solution.
Three-way Principal Components Analysis (PCA) allows for comprehension of variables influencing classification. The Partial Least Squares regression (PLS) combined with Discriminant Analysis (DA) allows classifying. Multivariate standard addition calibration based on PLS coupled with Net Analyte Signal (NAS) calculation allows bypassing the matrix effect in quantitative analysis.
This Chapter is focused on the issues mentioned above. Three sections will be presented:
SECTION 1: three-way PCA is applied to the discrimination among bacterial species in samples analyzed as such by pyrolysis gas chromatography-mass spectrometry. Applications to timely analysis of pathogenic microbes are foreseen.
SECTION 2 PLS-DA is applied to Raman spectra to discriminate adulterated beeswaxes from natural ones. This procedure may be implemented to prevent possible adulteration of bees’ products.
SECTION 3 PLS-NAS is applied to ATR spectra to quantify biogenic silica in marine sediments. The new method allows to accurately study the time evolution of primary productivity in the Antarctic basins.

The direct chemical analysis allows investigating samples without altering them, keeping the sample available for further analysis. For the qualitative investigation, analytical procedures like gas-chromatography, Raman microscopy, and Infra Red spectroscopy are available; however, the univariate approach is not exhaustive in the case of very complex matrices. The quantitative approach is still an open issue, due to the strong matrix effect hindering the creation of univariate calibration methods in interpolation mode. The multivariate analysis may be the solution.
Three-way Principal Components Analysis (PCA) allows for comprehension of variables influencing classification. The Partial Least Squares regression (PLS) combined with Discriminant Analysis (DA) allows classifying. Multivariate standard addition calibration based on PLS coupled with Net Analyte Signal (NAS) calculation allows bypassing the matrix effect in quantitative analysis.
This Chapter is focused on the issues mentioned above. Three sections will be presented:
SECTION 1: three-way PCA is applied to the discrimination among bacterial species in samples analyzed as such by pyrolysis gas chromatography-mass spectrometry. Applications to timely analysis of pathogenic microbes are foreseen.
SECTION 2 PLS-DA is applied to Raman spectra to discriminate adulterated beeswaxes from natural ones. This procedure may be implemented to prevent possible adulteration of bees’ products.
SECTION 3 PLS-NAS is applied to ATR spectra to quantify biogenic silica in marine sediments. The new method allows to accurately study the time evolution of primary productivity in the Antarctic basins.